Reimpression promotion system

ABSTRACT

In a promotion offering system, a promotion offering may be presented to a consumer on more than one occasion. The previous presentation of the promotion to the consumer may affect a subsequent presentation of the same promotion to the same consumer. The present invention provides an apparatus and method for analyzing the effect a previous presentation of a promotion may have on a consumer when the promotion is presented to the consumer at a subsequent time.

REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/675,769, filed Jul. 25, 2012, the entirety of which is herebyincorporated by reference herein.

FIELD OF THE INVENTION

The present description relates to offering promotions associated with aproduct or a service. This description more specifically relates topredicting a consumer's reaction to a presentation of a same, orsimilar, promotional offering that was previously presented to theconsumer.

BACKGROUND

Merchants typically offer promotions to consumers from time to time inorder to generate more business. The promotions offered may be in theform of discounts, deals, rewards or the like. Oftentimes, a promotion,or a number of promotions, may be presented to a consumer on more thanone occasion. When the same, or similar, promotions are offered to aconsumer, it may be difficult to determine how the consumer will reactto each subsequent presentation of the same, or similar, promotion.

SUMMARY OF THE INVENTION

A system and method is disclosed for providing a prediction of aconsumer's reaction to a presentation of a same, or similar, promotionthat was previously presented to the consumer.

According to an aspect of the present invention, a method fordetermining whether or how to offer a contemplated promotion in anelectronic correspondence to a consumer, with an amount of time elapsedbetween offering a promotion in a previous presentation and offering thecontemplated promotion, is provided. The method includes: generating anestimated acceptance by the consumer of the contemplated promotion;determining an effect of offering the promotion on the consumer'sacceptance of the contemplated promotion; combining the effect ofoffering the promotion and the estimated acceptance to generate anadjusted estimated acceptance by the consumer; and using the adjustedestimated acceptance in order to determine whether or how to include thecontemplated promotion in the electronic correspondence.

According to another aspect of the present invention, a system fordetermining whether or how to offer a contemplated promotion in anelectronic correspondence to a consumer, with an amount of time elapsedbetween offering a promotion in a previous presentation and offering thecontemplated promotion, is provided. The system includes: at least onememory configured to store a previous presentation data model and ahistorical data model, and a processor in communication with the atleast one memory. The processor is configured to: generate an estimatedacceptance by the consumer of the contemplated promotion; determine aneffect of offering the promotion on the consumer's acceptance of thecontemplated promotion; combine the effect of offering the promotion andthe estimated acceptance to generate an adjusted estimated acceptance bythe consumer; and use the adjusted estimated acceptance in order todetermine whether or how to include the contemplated promotion in theelectronic correspondence.

Other systems, methods, and features will be, or will become apparent toone with skill in the art upon examination of the following figures anddetailed description. It is intended that all such additional systems,methods, and features included within this description, be within thescope of the disclosure, and be protected by the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood with reference to thefollowing drawings and description. Non-limiting and non-exhaustivedescriptions are described with reference to the following drawings. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating principles. In the figures, likereferenced numerals may refer to like parts throughout the differentfigures unless otherwise specified.

FIG. 1A illustrates a representation of a network and a plurality ofdevices that interact with the network to achieve an analysis of howprevious presentations of a promotion may effect a consumer;

FIG. 1B illustrates a block diagram of one example of the analyticalmodel in FIG. 1A.

FIG. 1C illustrates a block diagram of another example of the analyticalmodel in FIG. 1A.

FIG. 2A illustrates a flow chart describing an overview of a process foranalyzing an effect of a previous presentation of a promotion to aconsumer, according to the present invention;

FIG. 2B illustrates a collection of correction factors; according to thepresent invention;

FIG. 2C illustrates a collection of performance data organized in theform of a lookup table, according to the present invention;

FIG. 3A illustrates a flow chart describing an overview of a process foranalyzing an effect of a previous presentation of a promotion to aconsumer, according to the present invention;

FIG. 3B illustrates a collection of correction factors; according to thepresent invention;

FIG. 3C illustrates a collection of performance data organized in theform of a lookup table, according to the present invention;

FIG. 4A illustrates a flow chart describing an overview of a process foranalyzing an effect of a previous presentation of a promotion to aconsumer, according to the present invention;

FIG. 4B illustrates a collection of correction factors; according to thepresent invention;

FIG. 4C illustrates a collection of performance data organized in theform of a lookup table, according to the present invention;

FIG. 5 illustrates a flow diagram describing an overview of a processfor extracting attributes of a consumer and a promotion, assigning thepromotion to a position within an electronic correspondence based on ascore of the promotion, and re-assigning the position of the promotionwithin the electronic correspondence based on a correction factor,according to the present invention;

FIG. 6 illustrates a flow diagram describing an overview of a processfor extracting attributes of a consumer and a promotion, determiningwhether to include the promotion in an electronic correspondence basedon a score of the promotion, and re-assessing the determination based ona correction factor, according to the present invention;

FIG. 7 illustrates a graph depicting a function for plotting a number ofpenalty multiplier adjustments, according to the present invention; and

FIG. 8 illustrates a general computer system, programmable to be aspecific computer system, which may represent any of the computingdevices referenced herein.

DETAILED DESCRIPTION

The present invention as described herein may be embodied in a number ofdifferent forms. Not all of the depicted components may be required,however, and some implementations may include additional, different, orfewer components from those expressly described in this disclosure.Variations in the arrangement and type of the components may be madewithout departing from the spirit or scope of the claims as set forthherein.

A promotion may include any type of reward, discount, coupon, credit,deal, voucher or the like used toward part (or all) of the purchase of aproduct or a service. The promotion may be offered as part of a largerpromotion program, or the promotion may be offered as a standaloneone-time promotion. In an effort to better distinguish and identify thepromotion, the promotion may include one or more attributes, such as themerchant offering the promotion (e.g., “XYZ coffee shop”), the locationof the promotion, the amount of the promotion (e.g., cost of acquiringor participating in the promotion), the category of the promotion (suchas a restaurant promotion, a spa promotion, a travel promotion, a localpromotion, etc.), the subcategory of the promotion (such as a sushirestaurant), amount of discount offered by the promotion, or the like.Likewise, an electronic correspondence that transmits the offer of thepromotion may have one or more attributes, including without limitation:a type of electronic correspondence (e.g., a promotion included in anemail, a webpage, etc.); a position assigned to the promotion within theelectronic correspondence (e.g., a first position at the beginning of anemail, a first position at the top of a webpage, etc.); the look, theme,appearance, or any other visual characteristics of the electroniccorrespondence that included the promotion; the time of day theelectronic correspondence including the promotion was presented to theconsumer; or the like. It should be noted that promotions and deals arerecited in this disclosure to be understood as being interchangeable,unless specifically stated otherwise.

FIG. 1A illustrates an overview for a promotion system 100 configured tooffer promotions for promotion programs. The promotion system 100includes a promotion offering system 102, which communicates via one ormore networks 122 with consumers, such as consumer 1 (124) to consumer N(126), and with merchants, such as merchant 1 (118) to merchant M (120).The promotion offering system 102 includes an analytical model 104 thatis in communication with databases 110, 112, 114, 116. The analyticalmodel 104 may include one or more components for analyzing thepromotions and determining which promotion(s) to send to the consumer.The analytical model 104 is configured to account for previouspresentation(s) to the consumer of the promotion (either the samepromotion or a similar promotion).

As discussed in more detail below, a promotion offering system 102 asillustrated in FIG. 1A may offer one or more promotions to a consumer ata given time, or at multiple times throughout a set time period. In thecase where multiple promotions are offered to the consumer, thepromotion offering system 102 may offer groupings of promotions in theform of promotion collections (such as offering, in a single electroniccorrespondence, multiple promotions selected from a promotioncollection). Examples of promotion collections may include, withoutlimitation, local promotions (e.g., promotions that are geographicallyproximate to the consumer or within a distance threshold to theconsumer), short term exclusive promotions, travel themed promotions,specific goods promotions (e.g., electronics, beauty products, sportsgoods, etc.), service deals, activity deals and holiday themedpromotions. In this way, the promotion offering system 102 may determinewhich promotion(s), or group(s) of promotions, to offer to a consumer.Examples of grouping promotions into promotion collections are disclosedin U.S. Provisional Patent Application No. 61/663,508, and U.S.application Ser. No. 13/838,415, entitled “Promotion Offering SystemAnalyzing Collections of Promotions”, both of which are incorporated byreference in their entirety. Going forward, all subsequent mention of apromotion may be interchangeable with a promotion collection.

The one or more promotions that are offered to the consumer may bepresented to the consumer in an electronic correspondence. Theelectronic correspondence may take the form of an email, SMS textmessage, webpage inbox message, VOIP voice message, real-time webpagecontent presentation, mobile push notifications or other similar typesof electronic correspondences where information is presented to, e.g.,“pushed” onto, a consumer. For illustrative purposes only, the followingdisclosure describes the electronic correspondence being presented tothe consumer in the form of an email that is transmitted to theconsumer. However, any type of electronic correspondence iscontemplated.

One way to present the promotions to the consumer is by sending anemail. The promotion offering system 102 may generate the email thatincludes one or more promotions intended to be viewed by a specificconsumer. The email may be tailored for the specific consumer byincluding promotion(s) that have been selected based on one or moreattributes of the consumer.

For instance, one or more attributes of the specific consumer may beused to generate an indication or estimate with respect to whether thespecific consumer will accept an offer for the promotion. One way togenerate an indication or estimate is to score promotions, with thehighest scoring promotion(s), indicating the highest estimate (e.g.,probability) of consumer acceptance, for inclusion in the email to thespecific consumer. The promotion may be scored according to any one ofthe methods disclosed in U.S. patent application Ser. No. 13/411,502 andU.S. Provisional Patent Application No. 61/644,352, both of which areincorporated by reference herein in their entirety. Other methods ofselecting the promotion(s) for inclusion in the email are contemplated.Further, a diverse mix of promotions may be provided to the consumer,such as discussed in U.S. Provisional Application No. 61/702,431 andU.S. application Ser. No. 13/841,433, entitled “Consumer Cross-CategoryDeal Diversity”, both of which are incorporated by reference herein.

The promotion offering system 102 may determine whether to include apromotion in a contemplated electronic correspondence. A contemplatedelectronic correspondence may refer to any electronic correspondence thepromotion offering system 102 considers sending to a consumer, e.g., viaany of the determination or processing performed by the promotionoffering system 102. In determining whether to include the promotion inthe contemplated electronic correspondence to a specific consumer (e.g.,via an email or a webpage), the promotion offering system 102 mayconsider whether the promotion was previously presented to the specificconsumer. The promotion offering system 102 may consider whether theexact same promotion (e.g., identical promotion) or a similar promotion(e.g., a promotion matching configurable similarity criteria, such asone or more matching promotion attributes) was previously presented tothe specific consumer. Presenting the promotion to the specific consumermay have an impact on the specific consumer's behavior for a subsequentpresentation of the same (or similar) promotion.

The promotion offering system 102 may consider one or more aspects ofthe previously-sent electronic correspondence and/or one or more aspectsof the contemplated electronic correspondence in determining whether toinclude the promotion in the contemplated electronic correspondence.Alternatively or in addition, the promotion offering system 102 mayconsider one or more aspects of the previously-sent electroniccorrespondence and/or one or more aspects of the contemplated electroniccorrespondence in determining a position within the contemplatedelectronic correspondence to assign to the promotion.

As discussed in more detail below, aspects of the previously-sentelectronic correspondence may include, without limitation, the time ofthe previously-sent electronic correspondence (e.g., how many days havepassed since the email including the promotion was previously sent), theposition of the promotion in the previously-sent electroniccorrespondence, the type of the previously-sent electroniccorrespondence, etc. Aspects of the contemplated electroniccorrespondence include, without limitation, the position of thepromotion in the contemplated electronic correspondence, the type of thecontemplated electronic correspondence, look, colors, or other visualcharacteristics used within the contemplated electronic correspondence,etc.

As one example, a contemplated email may include a single promotion ormultiple promotions. In the case of multiple promotions included in theemail, the email may be formatted with set positions for displaying eachof the promotions. In addition, each position within the email may bedesignated with a level of desirability. For example, positions that arecloser to the top of the email may be assigned a higher level ofdesirability than positions that are further down on the email. This isbecause a consumer that opens up the email has a higher likelihood ofviewing promotions that are displayed at the top of the email thanpromotions that are displayed closer to the bottom of the email.

In order to take advantage of more desirable positions within the email,promotions that have a higher score (e.g., conversion rate orprobability of a consumer purchasing the promotion) may be assigned topositions that are designated with higher levels of desirability. Forinstance, the promotion with the highest score may be assigned theposition with the highest level of desirability (e.g., the position atthe top of the email). Conversely, the promotion with the lowest scoremay be assigned the position with the lowest level of desirability.

Although only the email type of electronic correspondence has beendiscussed, the same positioning principles apply for any of the othertypes of electronic correspondences mentioned above. For instance, awebpage may also be formatted to include a set of positions that havedesignated levels of desirability. Promotions that are intended to bedisplayed on the webpage may be assigned to corresponding positions onthe webpage according to a score of each promotion.

The promotion offering system 102 may determine whether to include apromotion in a contemplated electronic correspondence, including throughthe analytical model 104. In one embodiment illustrated in FIG. 1B, theanalytical model 104 is configured to first analyze whether to send thepromotion to the consumer independent of whether the promotion waspreviously sent to the consumer (such as by using an odds model,discussed in FIGS. 5-6), and thereafter modify the analysis based onwhether the promotion (or similar promotion) was previously sent to theconsumer. More specifically, the analytical model 104 may be segmentedinto two separate models, a previous presentation data model 130 and ahistorical data model 132. Generally speaking, the previous presentationdata model 130 is configured to generate an effect of previouslypresenting a same or similar promotion to the consumer, and thehistorical data model 132 is configured to generate an estimatedacceptance of the contemplated promotion independent of whether the sameor similar promotion was previously presented.

The previous presentation data model 130 is configured to receive one ormore inputs, as illustrated in FIG. 1B. For example, the previouspresentation data model 130 is configured to receive the elapsed time,which is indicative of the amount of time that has elapsed betweenpreviously offering the same or similar promotion and offering thecontemplated promotion.

Optionally, the previous presentation data model may be organizedaccording to promotions attributes and/or consumer attributes. In thisregard, the previous presentation data model 130 may further beconfigured to receive one or more promotion attributes. As discussedherein, the analytical model 104 may account for previous presentationsof promotions in one of several ways. For example, the previouspresentation data model 130 may organize and/or store previousperformance data for all promotions. Thus, the previous presentationdata model 130 need not receive input promotion attributes from anexternal source since the previous presentation data model 130 isapplicable to all promotions. As another example, the previouspresentation data model 130 may organize and/or store previousperformance data for a subset of all promotions (such as promotions withone or more specific attributes). In this example, the previouspresentation data model 130 may receive input promotion attributes inorder to determine the applicable portion of the previous presentationdata model 130 for the promotion at issue (e.g., if the contemplatedpromotion has a specific attribute, such as a “restaurant” promotion,and the previous presentation data model 130 organizes the performancedata based on attributes, such as performance data for previouspresentations of restaurant promotions, the relevant performance datamay be accessed based on the promotion attribute input to the previouspresentation data model 130).

As still another option, the previous presentation data model 130 mayfurther be configured to receive one or more consumer attributes. Asdiscussed herein, the analytical model 104 may account for previouspresentations of promotions in one of several ways. For example, theprevious presentation data model 130 may organize previous performancedata for all consumers. So that, the previous presentation data model130 does not need to input consumer attributes since the previouspresentation data model 130 is applicable to all consumers. As anotherexample, the previous presentation data model 130 may organize previousperformance data for a subset of all consumers (such as consumers withone or more specific attributes). So that, the previous presentationdata model 130 may input promotion attributes in order to determine theapplicable portion of the previous presentation data model 130 for thespecific consumer (e.g., if the consumer has a specific attribute, suchas a “male” consumer, and the previous presentation data model 130organizes the performance data based on attributes, such as performancedata for previous presentations to different genders, the relevantperformance data may be accessed based on the consumer attribute inputto the previous presentation data model 130).

The historical data model 132 may receive one or more input promotionattributes and one or more input consumer attributes in order togenerate an estimated acceptance of the promotion. The estimatedacceptance of the promotion may be determined independent of whether thesame or similar promotion was previously presented.

In an alternate embodiment illustrated in FIG. 1C, the analytical model104 is configured to analyze whether to send the promotion to theconsumer, simultaneously accounting for factors with respect to factorsindependent of whether the promotion was previously sent to the consumerand factors based on whether the promotion was previously sent to theconsumer. In this embodiment, the analytical model 104 is configured toreceive one or more promotion attributes, the elapsed time, and one ormore consumer attributes. The analytical model 104 is configured tooutput the estimated acceptance by the consumer, accounting for theprevious presentation of a same or similar promotion to the consumer.

The analytical model 104 may thus determine the probability the consumerwill purchase each of the promotions, and select the promotion(s) toinclude in a contemplated electronic correspondence according to thedetermined probability. As another example, the analytical model 104 maydetermine the expectation value of the profit made if the promotion isoffered to the consumer, and select the promotion(s) to include in thecontemplated electronic correspondence according to the determinedexpectation values. As still another example, the analytical model 104may generate a score associated with presenting the promotion (with thescore being an indication of a probability that the specific consumerwill accept the promotion), and select the promotion(s) according to thedetermined scores.

The examples of analyses conducted by the analytical model 104 aremerely for illustrative purposes. Other types of analyses, disclosed inU.S. application Ser. No. 13/411,502, incorporated by reference hereinin its entirety, are contemplated.

The analytical model 104 may include a scoring model that is configuredto predict the likelihood that the consumer will accept a promotion thatis offered to the consumer independent of whether the consumer waspreviously presented with the same or similar promotion. The indicationof acceptance of the promotion, according to the scoring model, may takeone of several forms, such as the conversion rate (the rate by which aconsumer accepts a deal that is offered or the number of purchases ofthe deal divided by the number of times the deal is offered to users) oranother type of relevance score. The scoring model may be organized intodifferent categories of consumers correlated with different categories(and subcategories) of promotion types. For example, the analyticalmodel 104 may employ a scoring model that aggregates the historical datafrom previously-run promotions and/or historical data from the promotionunder consideration, organizing attributes of consumers (such as genderand distance from a promotion) with the conversion rates forcategories/subcategories of promotions. In particular, the scoring modelmay be segmented according to one or more user attributes (such as males0-2 miles from the promotion, males 2-4 miles from the promotion, etc.)and segmented by promotions in different categories/subcategories (suchas the category of restaurants, and the subcategories of Italianrestaurants, Greek restaurants, etc.). The analytical model 104aggregates the data from the previous promotions in order to generatethe conversion rates for the consumers in the different categories (suchas the conversion rate for consumers that are males 2-4 miles from aGreek restaurant deal in Chicago). The examples of the categories ofconsumers and the categories/subcategories of promotions are merely forillustration purposes only. Other categories are contemplated.

The analytical model 104 may further include one or more components forgenerating emails including the one or more promotions that have beenanalyzed, and also assigning a position within the email to each of theincluded promotions.

The analytical model 104 may likewise include one or more components foranalyzing the effect of previous presentation(s) of a promotion to aconsumer on a current presentation of the same, or similar, promotion,as described in more detail below. The analytical model 104 may use theeffect of previous presentation(s) to determine which promotion(s) tosend to the consumer. Alternatively or in addition, the analytical model104 may use the effect of previous presentations(s) to determine aposition within an electronic correspondence to assign each promotionincluded in the electronic correspondence. Further, the effect may berepresented in one of several ways, including a correction factor. Asdiscussed above, the analytical model 104 may generate a probability, anexpectation value, or a score independent of the effect of whether thepromotion was previously presented to the consumer. The effect of theprevious presentation(s), such as the correction factor, may be used tomodify the probability, expectation value, or score, thereby potentiallymodifying whether to include the promotion in the contemplated email, orre-assigning the promotion to a different position in the contemplatedemail.

In one embodiment, the analytical model 104 may generate theprobability, expectation value, or score using an odds model. Outputfrom the odds model may be interpreted as being independent of previouspresentation(s) of a promotion. In order to determine the effect of theprevious presentation(s), the output of the odds model for previouspresentations and the corresponding results of the previouspresentations are examined. For example, in generating the effect ofpresenting the same promotion 7 days ago, the analytical model 104 (oranother part of the promotion system 100) may analyze previouspresentations of a promotion that had a subsequent presentation of thesame promotion 7 days later. The set of previous presentations mayinclude: presentations for all promotions (regardless of category orsubcategory); or presentations for less than all promotions (such aspresentations for a specific category of deals, a specific subcategoryof deals, or another set of promotions that share one or more likeattributes). The analytical model 104 may analyze the output of the oddsmodel for the previous presentations (e.g., generate an average of theprobability, expectation value, or score derived from the odds model),and analyze the actual results of the subsequent presentation of thepromotion at a later time (e.g., generate an average of the actualresults of the subsequent presentation 7 days later). The analysis ofthe output of the odds model and the actual results may be compared,such as by calculating a ratio of the actual results to the output ofthe odds model, in order to generate a correction factor. The analyticalmodel 104 may apply the correction factor to determine the effect on theconsumer during the subsequent presentation (such as using thecomparison to generate the effect of a previously presentation of theexact same promotion 7 days earlier). In this way, subsequent uses ofthe odds model (such as using the odds model to determine whether toinclude a promotion in a contemplated email) may be modified by thecorrection factor in order to account for the previous presentation(s)of the promotion. Further description detailing the comparison of theoutput of the odds model and the actual results is provided throughoutthis disclosure. Moreover, using the output of the odds model and theactual results to determine the correction factor is merely one way todetermine the effect of the previous presentation.

To generate promotion scores, the analytical model 104 may communicatewith multiple databases that are part of (or work in conjunction with)the promotion offering system 102, such as a promotion programs database110, consumer profiles database 112, historical data database 114 anddynamic data database 116. The analytical model 104 may access thedatabases 110, 112, 114 and 116 in order to obtain specific attributeinformation for a specific consumer and the various promotions in thepromotion system 100. As described throughout this disclosure, variousattributes may be associated or assigned to a promotion and a specificconsumer in the promotion system 100. The obtained attribute informationmay then be utilized to generate promotion scores for each promotionwith respect to the specific consumer. As discussed above, the promotionscores are one example of an indication of the probability that thespecific consumer will accept an offer from a respective promotion.

The promotion programs database 110 is configured to store datadetailing various promotions and promotion programs that are availablefor offer in the promotion offering system 102. In order to inputpromotion program information into the promotions program database 110,merchants may optionally communicate via the networks 122 with thepromotion offering system 102 to input the information detailing thevarious promotion program offerings.

The consumer profiles database 112 includes profiles for the consumers,consumer 1 (124) to consumer N (126), that are included in the promotionsystem 100. The attribute information detailed for a consumer stored inthe consumer profiles database 112 may include, but is not limited to,name, age, address, occupation, educational background, previouslyaccepted promotion program offerings, previously rejected promotionprogram offerings, gender and the like. Any one, some or all of theattributes of the specific consumer may be used by the promotionoffering system 102 in determining whether to offer a promotion to thespecific consumer.

The historical data database 114 includes information detailing the pastperformance of promotion offerings that have been presented by thepromotion offering system 102. The historical data database 114 mayinclude, but is not limited to, rates of acceptances of specificpromotion programs, attributes of consumers that accepted or rejectedspecific promotion programs, and the like.

The dynamic data database 116 includes information detailing the pastperformance of a promotion program offering that is currently active inthe promotion offering system 102. So that, while a promotion programreferenced in the dynamic data database 116 is currently active, thedata stored in the dynamic data database 116 may include performancedata of the active promotion program from a previous time period. Forexample, promotions from a promotion program may be offered for a periodof 1 week. Consumer reaction (e.g., clicking on a link to the promotionin an email, purchase of the promotion, etc.) to offers for thepromotions transmitted on the first day may be stored in the dynamicdata database 116.

Although FIG. 1A has been illustrated to show separate databases 110,112, 114 and 116, FIG. 1A has been illustrated for demonstrativepurposes only, and it is contemplated to have the databases 110, 112,114 and 116 arranged in any combination of one or more memories/storageunits.

FIG. 2A illustrates a flow chart 200 describing an overview of a processfor analyzing how one or more previous presentations of a promotion to aconsumer may affect the behavior of the consumer for a subsequentpresentation of the same promotion. For exemplary purposes, acontemplated promotion is described as being transmitted to acontemplated consumer in a contemplated email. The promotion offeringsystem 102 or analytical model 104 may implement the process describedin flow chart 200 in hardware, software, firmware, or any combinationthereof.

In flow chart 200, the analysis for effect of previous presentation(s)is made by referencing a single attribute describing an amount of timesince the contemplated consumer has previously been presented with thecontemplated promotion. Further description is provided below.

At 201, it is determined whether the promotion being analyzed for thecontemplated email has been previously presented to the consumer. If thecontemplated promotion has not been previously presented to theconsumer, then the analysis ends and a correction factor is notaccessed.

However, if the determination at 201 finds that the contemplatedpromotion was previously presented to the consumer, then at 202 theprevious time at which the consumer was previously presented thecontemplated promotion is obtained. The previous time may be referencedas a particular date, time of day (e.g. morning, afternoon or night) orother similar measure of a time.

Alternatively or in addition, in some embodiments instead of determiningwhether the same contemplated promotion was previously presented to theconsumer, at 201 the analytical model 104 may determine whether apromotion that is similar to the contemplated promotion was previouslypresented to the consumer. A promotion may be considered to be similarto the contemplated promotion if, for example, the promotion shares oneor more attributes with the contemplated promotion (e.g., the promotionand the contemplated promotion both are for subcategory Chineserestaurants). Any configurable similarity criteria are contemplated. Inthese embodiments, performance data may be indicative of an effect ofre-presenting a promotion that shares one or more attributes with apreviously-presented promotion. A degree of similarity may correspond toa number of attributes shared between the contemplated promotion and thepreviously presented promotions. In some implementations, the analyticalmodel 104 may give one or more particular attributes greater or lesserweight when determining the degree of similarity. As described above,the process illustrated by flow chart 200 may be expanded to include adetermination of whether previous promotions that match or surpass a setdegree of similarity as the contemplated promotion were previouslypresented to the consumer.

After the previous time information is obtained at 202, at 203 an amountof time since the consumer was presented the contemplated promotion atthe previous time is calculated. The amount of time may be a differencebetween the current time for presenting the contemplated email and theobtained previous time. The amount of time may be measured according toa number of hours, days, weeks, months or other similar units of time.For exemplary purposes, the amount of time may be two days such that twodays have passed since the contemplated promotion has been presented tothe consumer.

In some embodiments, after 203, it may be determined whether the amountof time is greater than a predetermined amount, such as a configurableelapsed time threshold. If the amount of time is greater than thepredetermined amount, then the analysis ends and a correction factor isnot generated, or a correction factor having no effect may be generated(e.g. the correction factor is a multiplier equal to 1.0). In suchembodiments, the promotion offering system 102 or analytical model 104may be configured such that the predetermined amount of time is greatenough that the previous presentation of the contemplated promotion doesnot affect the consumer for current presentation of the contemplatedpromotion.

At 204, performance data that is indicative of an effect the previouspresentation of the contemplated promotion has had on the consumer maybe accessed. The accessed performance data according to flow chart 200may be based on the amount of time calculated at 203.

In some embodiments, the performance data may be organized as any typeof data construct, such as in the form of table 200C illustrated in FIG.2C. Further, the performance data may be organized based on the type ofpromotion (e.g., whether the promotion is the same as the contemplatedpromotion or whether the promotion is similar to the contemplatedpromotion) and/or based on the type of consumer (e.g., performance datafor all consumers, or performance data for a subset of consumers (suchas consumers that share one or more consumer attributes).

As discussed above, the analytical model 104 may examine whether thesame promotion was previously presented to the consumer. For example,the performance data may be based on the effect of re-presenting thesame promotion for all promotions issued by the promotion system 100over all consumers. For exemplary purposes, the performance datareferenced by the process described by flow chart 200 is based on suchperformance data.

Alternatively, the table (or other data construct) may be configured forperformance data for reactions from a subset of consumers that have beenpresented with the same promotion, such as consumers that have a sameattribute (or a same set of attributes). For example, the table may beconfigured for performance data of the same promotion for consumers 0-2miles from the promotion, or may be configured for performance data forconsumers that are male and age 30-39. Any combination of consumerattributes may be used in order to organize the performance data. Sothat, for performance data organized based on one or more consumerattributes, the performance data accessed is based on the attributes ofthe specific consumer for the contemplated promotion. For example, ifthe consumer is a female age 20-29, the analytical model 104 may accessperformance data for consumers that were presented with the samepromotion that have the attributes of a female age 20-29. As stillanother example, if the contemplated consumer has the attribute of beinga male, then the analytical model 104 may select performance dataconversion rates taken over all male consumers. By considering thegender attribute of the contemplated consumer when generating theperformance data according to this invention, the performance data maybe more relevant to the contemplated consumer. A list of consumerattributes includes, but is not limited to, gender, age or age range,location, past promotion purchases and consumer selected promotionfavorites.

Additionally or alternatively, the analytical model 104 may obtainperformance data characterizing the effect (as registered by allconsumers) of re-presenting a similar promotion (such as for all thepromotions in a specific category, a specific sub-category, or thepromotion at issue). In such embodiments, promotions that share one ormore attributes with the contemplated promotions may be considered to be“similar” promotions, and the performance data may be based on the moreconcise subset of similar promotions. For instance, if an attribute ofthe contemplated promotion identifies the contemplated promotion asbelonging to a restaurant promotions category, then the performance datamay only be selected for promotions that also share the attribute ofbelonging to the restaurant promotions category. By consideringattributes of the contemplated promotion when generating the performancedata, the performance data may be more relevant to the contemplatedpromotion. A list of promotion attributes includes, but is not limitedto, promotion sub-category, promotion location, promotion distance fromintended consumer and promotion availability.

In still another alternative, the performance data may be based on theeffect (as registered by a subset of all consumers, such as consumersthat share one or more attributes) of re-presenting a similar promotion.For example, the table may be configured for performance data of asimilar promotion for consumers 0-2 miles from the promotion, or may beconfigured for performance data for consumers that are male and age30-39. Any combination of consumer attributes may be used in order toorganize the performance data.

The performance data accessed at 204 may be represented by theconversion rates included in table 200C. Table 200C maps predictedconversion rates and actual conversion rates for promotions within thepromotion offering system 102. For instance, the predicted conversionrates may be based on the predictive odds model, and the actualconversion rates may be based on actual performance data. The historicalperformance score data may be stored and accessed from the historicaldata database 114.

In the example shown in FIG. 2C, promotion 1 in table 200C is seen tohave a predicted conversion rate of #₀₁ on day 0, where day 0 is thefirst day promotion 1 is presented to a consumer. The predictedconversion rate #₀₁ may, or may not, have been obtained prior to thepresentation of promotion 1 on day 0. The value of the predictedconversion rate #₀₁ may be obtained according to the odds model withoutany consideration for previous presentations of promotion 1 toconsumers. In column 1 (corresponding to day 1) of table 200C, theactual conversion rate for promotion 1 when promotion 1 was presented tothe consumer subsequently on day 1 is seen to be #₁₁.

Further, column 2 (corresponding to day 2) in table 200C illustrates theactual conversion rates for promotions that were presented to a consumertwo days following a previous presentation of the promotion to the sameconsumer. For instance, promotion 1 is seen to have an actual conversionrate of #₂₁ when promotion 1 was presented to a consumer two daysfollowing a previous presentation of promotion 1 to the same consumer.

Continuing the example discussed above, because the contemplatedpromotion was determined to have been previously presented to theconsumer two days ago (at 203), the performance data from column 2 andtheir respective predictive performance data in table 200C will beaccessed at 204. The performance data from column 2 in table 200Cidentifies conversion rates for promotions that were presented to aconsumer two days after a previous presentation of the promotion to thesame consumer.

From the performance data accessed at 204, a correction factor (CF) maybe generated at 205. For instance, based on the actual conversion ratedata referenced from column 2 of table 200C, the correction factor forthe contemplated promotion generated may be (0.13). The correctionfactor (0.13) for the contemplated promotion indicates that the actualconversion rate for all promotions that are presented to a consumer twodays following a previous presentation to the same consumer is thirteenpercent (0.13) of the corresponding predicted conversion rate. In termsof the performance scores provided in table 200C, the correction factor(0.13) for the contemplated promotion may be calculated as the ratio ofthe average of actual conversion rates found in column 2 versus theaverage of the respective predicted conversion rates in column 0.

Correction factors for various contemplated promotions may be organizedin any way, for example the lookup table 200B illustrated in FIG. 2B.The lookup table 200B includes a list of effects on performance scoresif the promotion is re-presented to a same consumer through correctionfactors. The correction factor is just one solution for accounting forthe effect a previous presentation of a promotion may have on a sameconsumer. Other solutions are contemplated. However for exemplarypurposes, lookup table 200B is seen to include correction factor valuesthat take into account the various lengths of time between presentationsof a promotion to a same consumer.

The first entry correction factor (0.09) in lookup table 200B is anindication that the actual conversion rate for all promotions that arepresented to a same consumer one day later is nine percent (0.09) of thecorresponding predicted conversion rate.

As mentioned above, the second entry correction factor (0.13) in lookuptable 200B is an indication that the actual conversion rate for allpromotions that are presented to a same consumer two days later isthirteen percent (0.13) of the corresponding predicted conversion rate.

Though FIG. 2A illustrates at 204 and 205 that the performance data isaccessed and the correction factor is generated, respectively, thecorrection data may be generated previously and stored in a lookup table200B. So that, 204 and 205 may be replaced by a single step of accessinglookup table 200B using the calculated amount of time from 203 todetermine the correction factor.

At 206, a determination is made as to whether the contemplated promotionhas been presented to the consumer at another previous time. If it isdetermined that the contemplated promotion was offered to the consumerat another previous time, this next previous time is obtained at 207.Following 207, the process from 203-206 is repeated.

When it is determined at 206 that there are no remaining previous timesat which the contemplated promotion was presented to the consumer, theprocess moves to 208. At 208, all of the generated correction factorsare applied to a historical conversion rate for the contemplatedpromotion. For exemplary purposes, the historical conversion rate forthe contemplated promotion may be considered to be twenty percent(0.20). In accordance to this example, the generated correction factor(0.13) is applied to the historical conversion rate of the contemplatedpromotion (0.20) in the following manner to obtain the adjustedconversion rate for the contemplated promotion:

Adjusted conversion rate=(0.20)×(0.13)=0.026

The contemplated promotion may have been presented to the consumer at asecond previous time. In cases where multiple correction factors aregenerated corresponding to the number of times the contemplatedpromotion was previous presented to the consumer, each previouspresentation of the contemplated promotion may be considered to be anindependent event. Subsequently, by considering each previouspresentation of the contemplated promotion as an independent event, eachcorresponding conversion rate may then be treated as originating from anindependent event (e.g., multiple correction factors are applied, witheach correction factor corresponding to a previous instance ofpresenting the promotion).

For exemplary purposes, an amount of time that has passed since a secondprevious time may be assumed to be three days. Referencing lookup table200B, the correction factor for the case where the contemplatedpromotion was previously presented to the same consumer three days agocan be seen to be (0.18). The correction factor (0.18) is an indicationthat the effect of the previous presentation results in eighteen percent(0.18) of the corresponding predicted conversion rate.

Using this correction factor (0.18) and the known historical conversionrate for the contemplated promotion of twenty percent (0.20), both thegenerated correction factor (0.18) and the previously generatedcorrection factor (0.13) may be applied to obtain the adjustedconversion rate for the contemplated promotion in the following manner:

Adjusted conversion rate=(0.20)×(0.13)×(0.18)=0.00468

Generally speaking, the adjusted conversion rate for the contemplatedpromotion may take on the following form:

Adjusted conversion rate=(Historical conversionrate)×(CF₁)×(CF₂)×(CF_(n))

whereby n is the number of times the contemplated promotion has beenpreviously presented to the consumer, and (CF_(n)) corresponds to thenth conversion rate generated.

The adjusted conversion rate for the contemplated promotion may becompared to conversion rates of other promotions in the promotion system100, and a determination of whether to include the contemplatedpromotion in the contemplated email may be made based on thiscomparison. As another example, the adjusted conversion rate for thecontemplated promotion may be compared to the conversion rates of otherpromotions included in the contemplated email, and a position of thecontemplated promotion within the contemplated email may be adjustedbased on this comparison.

FIG. 3A illustrates a flow chart 300 describing an overview of a processfor analyzing how one or more previous presentations of a promotion to aconsumer will affect the behavior of the consumer for a subsequentpresentation of the same promotion. For exemplary purposes, acontemplated promotion will be described as being transmitted to acontemplated consumer in a contemplated email. The promotion offeringsystem 102 or analytical model 104 may implement any portion of theprocess described in flow chart 300 in hardware, software, firmware, orany combination thereof.

In flow chart 300, the analysis references two attributes. The firstattribute describes an amount of time since the consumer has previouslybeen presented with the contemplated promotion. The second attributedescribes a position within a previous email that the contemplatedpromotion was assigned to when the contemplated promotion was previouslypresented to the consumer.

At 301, it is determined whether the contemplated promotion currentlybeing analyzed for the contemplated email has been previously presentedto the consumer at a previous time. If the contemplated promotion hasnot been previously presented to the consumer, then the analysis endsand a correction factor is not accessed.

However, if the determination at 301 finds that the contemplatedpromotion was previously presented to the consumer, then at 302 theprevious time at which the consumer was previously presented thecontemplated promotion in an email is obtained. The previous time may bereferenced as a particular date, time of day (e.g. morning, afternoon ornight) or other similar measure of a time. In addition to obtaining theprevious time information at 302, information identifying a position ofthe contemplated promotion within the previous email presentation to theconsumer is obtained. For exemplary purposes, the position at which thecontemplated promotion was assigned within a previous email to theconsumer may be position two.

Alternatively or in addition, in some embodiments instead of determiningwhether the same contemplated promotion was previously presented to theconsumer, at 301 the analytical model 104 may determine whether apromotion that is similar to the contemplated promotion was previouslypresented to the consumer. A promotion may be considered to be similarto the contemplated promotion if the promotion shares one or moreattributes with the contemplated promotion. In these embodiments,performance data, as will be described in further detail below, may beindicative of an effect of re-presenting a promotion that shares one ormore attributes of a previously-presented promotion. A degree ofsimilarity may correspond to a number of attributes shared between thecontemplated promotion and the other promotions, as discussed above. Inthis way, the process illustrated by flow chart 300 may be expanded toinclude a determination whether other promotions that match or surpass aset degree of similarity as the contemplated promotion, were previouslypresented to the consumer at a previous time.

After the previous time and position information are obtained at 302, at303 an amount of time since the consumer was presented the contemplatedpromotion at the previous time is calculated. The amount of time may bea difference between the current time for presenting the contemplatedemail and the obtained previous time. The amount of time may be measuredaccording to a number of hours, days, weeks, months or other similarunits of time. For exemplary purposes, the amount of time may be twodays such that two days have passed since the contemplated promotion hasbeen presented to the consumer.

In some embodiments, after 303, it may be determined whether the amountof time is greater than a predetermined amount, as described above. Ifthe amount of time is greater than the predetermined amount, then theanalysis ends and a correction factor is not generated, or a correctionfactor having no effect may be generated (e.g. the correction factor isa multiplier equal to 1.0). In such embodiments, the promotion offeringsystem 102 is configured such that the predetermined amount of time isgreat enough that the previous presentation of the contemplatedpromotion does not affect the consumer for current presentation of thecontemplated promotion.

At 304, performance data that is indicative of an effect the previouspresentation of the contemplated promotion has had on the consumer maybe accessed. The accessed performance data according to flow chart 300may be based on the amount of time calculated at 303 and the positioninformation obtained at 302.

In some embodiments, the performance data may be organized as any typeof data construct, such as in the form of table 300C illustrated in FIG.3C. As discussed above, the analytical model 104 may examine whether theexact promotion was previously presented to the consumer. For example,the performance data may be based on the effect of re-presenting thesame promotion in a same position within an email for any promotionsissued by the promotion system 100 over all consumers. For exemplarypurposes, the performance data referenced by the process described byflow chart 300 is based on such performance data. However, theperformance data may be adjusted in any combination of the waysdescribed with reference to flow chart 200 above.

The performance data accessed at 304 may be represented by theconversion rates included in table 300C. Table 300C maps predictedconversion rates and actual conversion rates for promotions within thepromotion offering system 102 taking into consideration two attributes.The first attribute is the amount of time since the consumer waspresented the contemplated promotion. And the second attribute is theposition in which the contemplated promotion was assigned in theprevious presentation. Also, the predicted conversion rates may be basedon the predictive odds model, and the actual conversion rates may bebased on real historical performance data. The historical performancescore data may be stored and accessed from the historical data database114.

In the example shown in FIG. 3A, promotion 1 in table 300C is seen tohave a predicted conversion rate of #₀₁ on day 0, where day 0 is thefirst day promotion 1 is presented to a consumer. Additionally, theconversion rate data in table 300C will account for the position in theemail in which the contemplated promotion was assigned in the previouselectronic correspondence (email). The predicted conversion rate #₀₁may, or may not, have been obtained prior to the presentation ofpromotion 1 on day 0. The value of the predicted conversion rate #₀₁ maybe obtained according to the odds model without any consideration forprevious presentations of promotion 1 to consumers. In column 1(corresponding to day 1) of table 300C, the actual conversion rate forpromotion 1 when promotion 1 was presented to the consumer, in the sameposition within the email as the corresponding promotion, subsequentlyon day 1 is seen to be #₁₁.

Further, column 2 (corresponding to day 2) in table 300C illustrates theactual conversion rates for promotions that were presented to aconsumer, in the same position within the email as the correspondingpromotion, two days following a previous presentation of the promotionto the same consumer. For instance, promotion 1 is seen to have anactual conversion rate of #₂₁ when promotion 1 was presented to aconsumer, in the second position within the email, two days following aprevious presentation of promotion 1 to the same consumer.

According to the current example, because the contemplated promotion wasdetermined to have been previously presented to the consumer two daysago (at 303) in the second position of the email, the performance datafrom column 2 and their respective predictive performance data in table300C will be accessed at 304. The performance data from column 2 intable 300C identifies conversion rates for promotions that werepresented to a consumer in position two of an email and two days after aprevious presentation of the promotion to the same consumer.

From the performance data accessed at 304, a correction factor (CF) maybe generated at 305. For instance, based on the actual conversion ratedata referenced from column 2 of table 300C, the correction factor forthe contemplated promotion generated may be (0.10). The correctionfactor (0.10) for the contemplated promotion indicates that the actualconversion rate for all promotions that are presented to a consumer inposition two and two days following a previous presentation to the sameconsumer is ten percent (0.10) of the corresponding predicted conversionrate. In terms of the performance scores provided in table 200C, thecorrection factor (0.10) for the contemplated promotion may becalculated as the ratio of the average of actual conversion rates foundin column 2 versus the average of the respective predicted conversionrates in column 0 (corresponding to day 0).

Correction factors for various contemplated promotions may be organizedin any way, for example as the lookup table 300B illustrated in FIG. 3B.The lookup table 300B includes a list of effects on performance scoresif the promotion is re-presented. The correction factor is just onesolution for accounting for the effect a previous presentation of apromotion may have on a same consumer. Other solutions are alsocontemplated. However for exemplary purposes, lookup table 300B is seento include correction factor values that take into account the twoattributes mentioned above. For instance, the correction factor (0.10)for the contemplated promotion is seen to be located at (2, 2) in thelookup table 300B. The location (2, 2) identifies that the correspondingcorrection factor takes into account promotions that were presented to asame consumer two days apart, and promotions that were presented in thesecond position of an email.

As seen in FIG. 3B, the correction factor located at (1, 1) in thelookup table 300B is seen to have the value (0.09), and is an indicationthat the actual conversion rate for all promotions that are presented toa same consumer in position one of an email and one day later is ninepercent (0.09) of the corresponding predicted conversion rate.

As mentioned above, the correction factor located at (2, 2) in lookuptable 300B is seen to have the value (0.10), and is an indication thatthe actual conversion rate for all promotions that are presented to asame consumer in position two of an email and two days later is tenpercent (0.10) of the corresponding predicted conversion rate.

Though FIG. 3A illustrates at 304 and 305 that the performance data isaccessed and the correction factor is generated, respectively, thecorrection data may be generated previously and stored in a lookup table300B. So that, 304 and 305 may be replaced by a single step of accessinglookup table 300B using the calculated amount of time from 203 todetermine the correction factor.

At 306, a determination is made as to whether the contemplated promotionhas been presented to the consumer at another previous time. If it isdetermined that the contemplated promotion was offered to the consumerat another previous time, this next previous time is obtained at 307.Following 307, the process from 303-306 is repeated.

When it is determined at 306 that there are no remaining previous timesat which the contemplated promotion was presented to the consumer, theprocess moves to 308. At 308, all of the generated correction factorsmay be applied to a historical conversion rate for the contemplatedpromotion. For exemplary purposes, the historical conversion rate forthe contemplated promotion may be considered to be twenty percent(0.20). In accordance to this example, the generated correction factor(0.10) is applied to the historical conversion rate of the contemplatedpromotion (0.20) in the following manner to obtain the adjustedconversion rate for the contemplated promotion:

Adjusted conversion rate=(0.20)×(0.10)=0.02

The contemplated promotion may have been presented to the consumer at asecond previous time. In cases where multiple correction factors aregenerated corresponding to the number of times the contemplatedpromotion was previous presented to the consumer, each previouspresentation of the contemplated promotion may be considered to be anindependent event. Subsequently, by considering each previouspresentation of the contemplated promotion as an independent event, eachcorresponding conversion rate may then be treated as originating from anindependent event (e.g., multiple correction factors are applied, witheach correction factor corresponding to a previous instance ofpresenting the promotion).

For exemplary purposes, an amount of time that has passed since a secondprevious time may be assumed to be three days, and a position within theprevious email may be assumed to be the third position. Referencinglookup table 300B, the correction factor for the case where thecontemplated promotion was previously presented to the same consumerthree days ago in position three of an email can be seen to be (0.09).The correction factor (0.09) is an indication that the effect of theprevious results in nine percent (0.09) of the corresponding predictedconversion rate.

Using this correction factor (0.09) and the known historical conversionrate for the contemplated promotion of twenty percent (0.20), both thegenerated correction factor (0.09) and the previously generatedcorrection factor (0.10) may be applied to obtain the adjustedconversion rate for the contemplated promotion in the following manner:

Adjusted conversion rate=(0.20)×(0.10)×(0.09)=0.0018

Generally speaking, the adjusted conversion rate for the contemplatedpromotion may take on the following form:

Adjusted conversion rate=(Historical conversion rate)×(CF₁)×(CF₂)× . . .(CF_(n))

whereby n is the number of times the contemplated promotion has beenpreviously presented to the consumer, and (CF_(n)) corresponds to thenth conversion rate generated.

The adjusted conversion rate for the contemplated promotion may becompared to conversion rates of other promotions in the promotion system100, and a determination of whether to include the contemplatedpromotion in the contemplated email may be made based on thiscomparison. Alternatively, the adjusted conversion rate for thecontemplated promotion may be compared to the conversion rates of otherpromotions included in the contemplated email, and a position of thecontemplated promotion within the contemplated email may be adjustedbased on this comparison.

FIG. 4A illustrates a flow chart 400 describing an overview of a processfor analyzing how one or more previous presentations of a promotion to aconsumer will affect the behavior of the consumer for a subsequentpresentation of the same promotion. For exemplary purposes, acontemplated promotion is described as being transmitted to acontemplated consumer in a contemplated email. The promotion offeringsystem 102 or analytical model 104 may implement any portion of theprocess described in flow chart 400 in hardware, software, firmware, orany combination thereof.

In flow chart 400, the analysis references three attributes. The firstattribute describes an amount of time since the consumer has previouslybeen presented with the contemplated promotion. The second attributedescribes a position within a previous email that the contemplatedpromotion was assigned to when the contemplated promotion was previouslypresented to the consumer. The third attribute considers thecontemplated position of the contemplated promotion in the contemplatedemail. Further description is provided below.

At 401, it is determined whether the contemplated promotion currentlybeing analyzed for the contemplated email has been previously presentedto the consumer at a previous time. If the contemplated promotion hasnot been previously presented to the consumer, then the analysis endsand a correction factor is not generated nor required.

However, if the determination at 401 finds that the contemplatedpromotion was previously presented to the consumer, then at 402 acontemplated position for the contemplated promotion within thecontemplated email is selected. For exemplary purposes, the system mayconsider assigning the contemplated promotion to the first position (P1)in the contemplated email.

Alternatively or in addition, in some embodiments instead of determiningwhether the same contemplated promotion was previously presented to theconsumer, at 401 the analytical model 104 may determine whether apromotion that is similar to the contemplated promotion was previouslypresented to the consumer, as described above. In these embodiments,performance data, as will be described in further detail below, may beindicative of an effect of re-presenting a promotion that shares one ormore attributes of a previously-presented promotion. A degree ofsimilarity may correspond to a number of attributes shared between thecontemplated promotion and the other promotions. In this way, theprocesses illustrated by flow chart 400 may be expanded to include adetermination whether other promotions that match or surpass a setdegree of similarity as the contemplated promotion, were previouslypresented to the consumer at a previous time.

At 403 the previous time at which the consumer was previously presentedthe contemplated promotion in an email is obtained. The previous timemay be referenced as a particular date, time of day (e.g. morning,afternoon or night) or other similar measure of a time. In addition toobtaining the previous time information at 403, information identifyinga position of the contemplated promotion within the previous emailpresentation to the consumer is obtained. For example, the contemplatedpromotion was assigned to position two within a previous email to theconsumer.

After the previous time and position information are obtained at 403, at404 an amount of time since the consumer was presented the contemplatedpromotion at the previous time is calculated. The amount of time may bea difference between the current time for presenting the contemplatedemail and the obtained previous time. The amount of time may be measuredaccording to a number of hours, days, weeks, months or other similarunits of time. For exemplary purposes, the amount of time may be twodays such that two days have passed since the contemplated promotion hasbeen presented to the consumer.

In some embodiments, after 404, it may be determined whether the amountof time is greater than a predetermined amount (not illustrated). If theamount of time is greater than the predetermined amount, then theanalysis ends and a correction factor is not generated, or a correctionfactor having no effect may be generated (e.g. the correction factor isa multiplier equal to 1.0). In such embodiments, the promotion offeringsystem 102 is configured such that the predetermined amount of time isgreat enough that the previous presentation of the contemplatedpromotion does not affect the consumer for current presentation of thecontemplated promotion.

At 405, performance data that is indicative of an effect the previouspresentation of the contemplated promotion has had on the consumer maybe accessed. In flow chart 400, the accessed performance data may bebased on the position of the contemplated email selected at 402, theamount of time calculated at 404 and the position information obtainedat 403.

In some embodiments, the performance data may be organized as any typeof data construct, such as in the form of table 400C illustrated in FIG.4C. As discussed above, the analytical model 104 may examine whether theexact promotion was previously presented to the consumer. For example,the performance data may be based on the effect of re-presenting thesame promotion in a same position within an email for any promotionsissued by the promotion system 100 over all consumers, and alsoconsidering a contemplated position within the contemplated email forassigning the contemplated promotion. For exemplary purposes, theperformance data referenced by the process described by flow chart 400is based on such performance data. However, the performance data may beadjusted in any combination of the ways described with reference to flowchart 200 above.

The performance data accessed at 405 may be represented by theconversion rates included in table 400C. Table 400C maps predictedconversion rates and actual conversion rates for promotions within thepromotion offering system 102, taking into consideration threeattributes. The first attribute is the amount of time since the consumerwas presented the contemplated promotion. The second attribute is theposition in which the contemplated promotion was assigned in theprevious presentation. And the third attribute is a contemplatedposition within the contemplated email for assigning the contemplatedpromotion. Also, the predicted conversion rates may be based on thepredictive odds model, and the actual conversion rates may be based onreal historical performance data. The historical performance score datamay be stored and accessed from the historical data database 114.

In the example shown in FIG. 4C, promotion 1 in table 400C is seen tohave a predicted conversion rate of #₀₁ on day 0, where day 0 is thefirst day promotion 1 is presented to a consumer. Additionally, theconversion rate data in table 300C will account for the position in theemail in which the contemplated promotion was assigned in the previouselectronic correspondence (email), and the contemplated position withinthe contemplated email for assigning the contemplated promotion. Thepredicted conversion rate #₀₁ may, or may not, have been obtained priorto the presentation of promotion 1 on day 0. The value of the predictedconversion rate #₀₁ may be obtained according to the odds model withoutany consideration for previous presentations of promotion 1 toconsumers. In column 1 (corresponding to day 1) of table 300C, theactual conversion rate for promotion 1 when promotion 1 was presented tothe consumer, in the same position within the email as the correspondingpromotion, subsequently on day 1 is seen to be #₁₁.

Further, column 2 (corresponding to day 2) in table 400C illustrates theactual conversion rates for promotions that were presented to aconsumer, in the same position within the email as the correspondingpromotion, two days following a previous presentation of the promotionto the same consumer. For instance, promotion 1 is seen to have anactual conversion rate of #₂₁ when promotion 1 was presented to aconsumer, in the second position within the email, two days following aprevious presentation of promotion 1 to the same consumer.

According to the current example, because the contemplated promotion wasdetermined to have been previously presented to the consumer two daysago (at 404) in the second position of the email, the performance datafrom column 2 and their respective predictive performance data in table400C will be accessed at 405. The performance data from column 2 intable 400C identifies conversion rates for promotions that werepresented to a consumer in position two of an email and two days after aprevious presentation of the promotion to the same consumer. Theconversion rates included in table 400C also takes into account theadded variable of the position within the contemplated email that isbeing contemplated for assigning the contemplated promotion.

From the performance data accessed at 405, a correction factor (CF) maybe generated at 406. For instance, based on the actual conversion ratedata referenced from column 2 of table 400C, the correction factor forthe contemplated promotion generated may be (0.10). The correctionfactor (0.10) for the contemplated promotion indicates that the actualconversion rate for all promotions that are presented to a consumer inposition two and two days following a previous presentation to the sameconsumer is ten percent (0.10) of the corresponding predicted conversionrate. In terms of the performance scores provided in table 400C, thecorrection factor (0.10) for the contemplated promotion may becalculated as the ratio of the average of actual conversion rates foundin column 2 versus the average of the respective predicted conversionrates in column 0.

Correction factors for various contemplated promotions may be organizedin any way, for example as the lookup table 400B-1 illustrated in FIG.4B, with and additional lookup tables 400B-2 and 400B-3 being madeavailable as needed. Lookup tables 400B-1, 400B-2 and 400B-3 include alist of effects on performance scores if the promotion is re-presented.The correction factor is just one solution for accounting for the effecta previous presentation of a promotion may have on a same consumer.Other solutions are also contemplated. However for exemplary purposes,lookup tables 400B-1, 400B-2 and 400B-3 are seen to include correctionfactor values that take into account the three attributes mentionedabove. For instance, the correction factor (0.10) for the contemplatedpromotion is seen to be located at (2, 2) in the lookup table 400B-1.The location (2, 2) identifies that the corresponding correction factortakes into account promotions that were presented to a same consumer twodays apart, and promotions that were presented in the second position ofan email. The correction factor (0.10) located at (2, 2) in the lookuptable 400B-1 also takes into account that the contemplated promotion isbeing contemplated for the first position (P1) within the contemplatedemail as selected at 402.

Lookup tables 400B-1, 400B-2 and 400B-3 take into consideration thecontemplated position for the contemplated promotion within thecontemplated email. For instance, lookup table 400B-1 may include theperformance data representing historical conversion rates over allpromotions given the two attributes mentioned above, with the addedconsideration of assigning the contemplated promotion in position one(P1) of the contemplated email. Then lookup table 400B-2 may include theperformance data representing historical conversion rates over allpromotions given the two attributes mentioned above, with the addedconsideration of assigning the contemplated promotion in position two(P2) of the contemplated email, and so forth.

According to the current example, because the contemplated promotionbeing analyzed in flow chart 400B was previously presented to theconsumer two days ago, and the position of the contemplated promotionwas in position two of the previous email, and the contemplatedpromotion is being considered for the first position within thecontemplated email, performance data located at (2, 2) in lookup table400B-1 will be accessed. This performance data is seen to be (0.10).

Though FIG. 4A illustrates at 405 and 406 that the performance data isaccessed and the correction factor is generated, respectively, thecorrection data may be generated previously and stored in a lookup table400B. So that, 405 and 406 may be replaced by a single step of accessinglookup table 400B using the calculated amount of time from 404 todetermine the correction factor.

At 407, a determination is made as to whether the contemplated promotionhas been presented to the consumer at another previous time. If it isdetermined that the contemplated promotion was offered to the consumerat another previous time, this next previous time is obtained at 408.Following 408, the process from 404-407 is repeated.

When it is determined at 407 that there are no other previous times atwhich the contemplated promotion was presented to the consumer, theprocess moves to 409. At 409, all of the generated correction factorsare applied to the historical conversion rate for the contemplatedpromotion. For exemplary purposes, the historical conversion rate forthe contemplated promotion may be considered to be twenty percent(0.20). In accordance to this example, the generated correction factor(0.10) is applied to the historical conversion rate of the contemplatedpromotion (0.20) in the following manner to obtain the adjustedconversion rate for the contemplated promotion:

Adjusted conversion rate=(0.20)×(0.10)=0.02

The contemplated promotion may have been presented to the consumer at asecond previous time. In cases where multiple correction factors aregenerated corresponding to the number of times the contemplatedpromotion was previous presented to the consumer, each previouspresentation of the contemplated promotion may be considered to be anindependent event. Subsequently, by considering each previouspresentation of the contemplated promotion as an independent event, eachcorresponding conversion rate may then be treated as originating from anindependent event (e.g., multiple correction factors are applied, witheach correction factor corresponding to a previous instance ofpresenting the promotion).

For exemplary purposes, an amount of time that has passed since a secondprevious time may be assumed to be three days, and a position within theprevious email may be assumed to be the third position. Referencinglookup table 400B, the correction factor for the case where thecontemplated promotion was previously presented to the same consumerthree days ago in position three of an email, and the contemplatedpromotion is being contemplated for the first position (P1) within thecontemplated email, can be seen to be (0.09). The correction factor(0.09) is an indication that the effect of the previous presentationresults in nine percent (0.09) of the corresponding predicted conversionrate.

Using this correction factor (0.09) and the known historical conversionrate for the contemplated promotion of twenty percent (0.20), both thegenerated correction factor (0.09) and the previously generatedcorrection factor (0.10) may be applied to obtain the adjustedconversion rate for the contemplated promotion in the following manner:

Adjusted conversion rate=(0.20)×(0.10)×(0.09)=0.0018

Generally speaking, the adjusted conversion rate for the contemplatedpromotion may take on the following form:

Adjusted conversion rate=(Historical conversionrate)×(CF₁)×(CF₂)×(CF_(n))

whereby n is the number of times the contemplated promotion has beenpreviously presented to the consumer, and (CF_(n)) corresponds to thenth conversion rate generated.

The adjusted conversion rate for the contemplated promotion may becompared to conversion rates of other promotions in the promotion system100, and a determination of whether to include the contemplatedpromotion in the contemplated email may be made based on thiscomparison. Alternatively, the adjusted conversion rate for thecontemplated promotion may be compared to the conversion rates of otherpromotions included in the contemplated email, and a position of thecontemplated promotion within the contemplated email may be adjustedbased on this comparison.

After applying all of the correction factors at 409, at 410 adetermination is made whether to perform the analysis described by flowchart 400 for another position assignment of the contemplated promotionwithin the contemplated email. If another position within thecontemplated email is to be considered, the process revisits 402. Theanalysis for the new position assignment of the contemplated promotionmay utilize a corresponding lookup table in FIG. 4B that corresponds tothe new position assignment. For instance, if an analysis is to beaccomplished for assigning the contemplated promotion in the secondposition within the contemplated email, lookup table 400B-2 may bereferenced. If another position within the contemplated email is not tobe considered, the process ends.

FIG. 5 illustrates a flow diagram 500 describing an overview of aprocess for assigning a contemplated promotion to a position within acontemplated email. The promotion offering system 102 or analyticalmodel 104 may implement any portion of the process described in flowdiagram 500 in hardware, software, firmware, or any combination thereof.

At 501, a contemplated promotion and a contemplated consumer forreceiving the contemplated promotion are selected. Attributes of thecontemplated consumer and the contemplated promotion are extracted.Examples of consumer attributes and promotion attributes, which are notexhaustive, are provided throughout this description.

At 502, the extracted attributes of the contemplated consumer and thecontemplated promotion are used in combination with the odds model,discussed above, in order to score the contemplated promotion. The scorefor the contemplated promotion may be an indication of a probability thecontemplated consumer will purchase the contemplated promotion. Once thecontemplated promotion is scored, the contemplated promotion may beassigned a position within the contemplated email. The position assignedto the contemplated promotion may be based on an analysis comparing thescore of the contemplated promotion against the scores for otherpromotions included in the contemplated email. The order of assigningpositions to promotions included in an email, or other electroniccorrespondence, is provided above.

At 503, an analysis that considers how a previous presentation of thecontemplated promotion may affect the behavior of the contemplatedconsumer is made. The processes described in flow charts 200, 300 and400 are encompassed, at least in part, by the analysis described at 503.From this analysis, a correction factor may be accessed. The correctionfactor may then be applied to the score of the contemplated promotionobtained prior at 502. After the correction factor is applied to thescore of the contemplated promotion, a new promotion score of thecontemplated promotion is obtained. Based on this new score, theposition of the contemplated promotion may be re-assigned.

FIG. 6 illustrates an alternative flow diagram 600 describing anoverview of a process for determining whether to include a contemplatedpromotion within a contemplated email. The promotion offering system 102or analytical model 104 may implement any portion of the processdescribed in flow diagram 600 in hardware, software, firmware, or anycombination thereof.

At 601, a contemplated promotion and a contemplated consumer forreceiving the contemplated promotion are selected. Attributes of thecontemplated consumer and the contemplated promotion are extracted.Examples of consumer attributes and promotion attributes, which are notexhaustive, are provided throughout this description.

At 602, the extracted attributes of the contemplated consumer and thecontemplated promotion are used in combination with the odds model,discussed above, in order to score the contemplated promotion. The scorefor the contemplated promotion may be an indication of a probability thecontemplated consumer will purchase the contemplated promotion. Once thecontemplated promotion is scored, a determination may be made as towhether to include the contemplated promotion in the contemplated email.This determination may be made based on an analysis comparing the scoreof the contemplated promotion against the scores for other promotionsincluded in the contemplated email. If the score indicates that thecontemplated promotion is one of the top promotions amongst a set numberof promotions that are to be included in the contemplated email, thecontemplated promotion may be included in the contemplated email.Alternatively, the contemplated promotion may be included in thecontemplated email if the contemplated promotion's score is greater thana set value.

At 603, an analysis that considers how a previous presentation of thecontemplated promotion may affect the behavior of the contemplatedconsumer is made. The processes described in flow charts 200, 300 and400 are encompassed, at least in part, by the analysis described at 603.From this analysis, a correction factor may be accessed. The correctionfactor may then be applied to the score of the contemplated promotionobtained prior at 602. After the correction factor is applied to thescore of the contemplated promotion, a new promotion score of thecontemplated promotion is obtained. Based on this new score, thedetermination of whether to include the contemplated promotion in thecontemplated email may be reassessed. For instance, if the correctionfactor brings the score of the contemplated promotion down, thecontemplated promotion may lose its spot in the contemplated email.Conversely, if the correction factor brings the score of thecontemplated promotion up, the contemplated promotion may gain a spot inthe contemplated email.

It is further noted that although the description specificallyconsidered the effect of previous presentations of a same, or similar,promotion to a same consumer, the processes and analyses described mayalso be applied to determine the effect of previous presentations of asame, or similar, collection of promotions to a same consumer. Thecontemplated email may include one or more promotion collections thatmay have been scored and analyzed previously, and going forward, as agroup. In such cases, the same processes and analyses described abovefor individual promotions may be applied to the promotion collections.

Although not specifically illustrated in flow charts 200, 300 or 400, anoptional analysis may be added following the application of thecorrection factor(s), or at any time following the generation of thecorrection factor(s). This optional analysis applies a penalty to thecorrection factor(s) generated during the processes described in flowcharts 200, 300 and 400. The penalty is applied to account for theassumption that consumers become more receptive to purchasing promotionsas the time between the presentations of a same promotion increases. Inthis way, the penalty will decrease as the time between presentations ofthe same promotion increases. The penalty may be applied to generate anew adjusted conversion rate for the contemplated promotion.

Alternatively or in addition, the penalty may be applied to account forthe assumption that consumers become more receptive to purchasingpromotion as the time between presentations of a similar promotionincreases. In this way, the penalty will decrease as the time betweenpresentations of similar promotions increases. The penalty may beapplied to generate a new adjusted conversion rate for the contemplatedpromotion. A previously presented promotion may be determined to besimilar to the contemplated promotion based on the sharing of one ormore attribute. A list of promotion attributes may include, but is notlimited to, promotion category, promotion sub-category, position withinthe electronic correspondence assigned to the promotion, location of thepromotion, amount of discount offered by the promotion, look of theelectronic correspondence, time of day the electronic correspondenceincluding the promotion was presented to the consumer, and so on.

The penalty may take on any number of forms, such as, for example, apenalty multiplier (PM) that is applied either to a correction factor(CF) or an adjusted conversion rate (ACR) that is obtained during theexecution of any one of the processes described above in flow charts200, 300 and 400.

In cases where the penalty multiplier is applied to the correctionfactor, the penalty multiplier may be applied in the following manner toobtain an adjusted correction factor:

Adjusted correction factor=(CF)×(PM)

In cases where the penalty multiplier is applied to the adjustedconversion rate, the penalty multiplier may be applied in the followingmanner to obtain a new adjusted conversion rate:

New adjusted conversion rate=(ACR)×(PM)

An example of a function mapping a value of the penalty multiplier isshown in graph 700 illustrated in FIG. 7. The exemplary function ismapped across an amount of time that has passed since a same, orsimilar, promotion has been presented to a consumer (x-axis) against thepenalty multiplier value (y-axis). The amount of time may take on theform of a number of years, months, days, hours, minutes or other likeunit of time. For exemplary purposes, the unit of days will bereferenced. The graph specifically illustrates the function as series 1,where series 1 may correspond to the penalty multipliers to be appliedfor the case where the contemplated promotion is being considered for aspecific position (e.g. P1) within the contemplated email. A uniquefunction mapping values of a penalty multiplier may be generated foreach contemplated position within the contemplated email beingconsidered to assign the contemplated promotion. Alternatively or inaddition, a unique function mapping penalty multiplier values may begenerated for each set of shared attributes that are considered whendetermining whether a previous promotion is similar to the contemplatedpromotion.

The function mapping the penalty multiplier has a number of parametersthat may be adjusted based on the historical data obtained for the same,or similar, promotions that are being considered for the penaltyanalysis. For instance, the minimum penalty multiplier parameter may beadjusted. Also, the maximum penalty multiplier parameter may beadjusted. Also the inflection point of the penalty multiplier functionmay be adjusted. And a beta parameter that controls the slope of thepenalty multiplier function may be adjusted. In the penalty multiplierfunction illustrated in the graph 700, the minimum penalty multiplier isset to (0.01), the maximum penalty multiplier is set to (0.90), and theinflection point is set to occur at 37 days.

The inflection point may be, as one example, the point where the penaltymultiplier becomes: (minimum penalty multiplier)+(maximum penaltymultiplier−minimum penalty multiplier)/2

FIG. 8 illustrates a general computer system 800, programmable to be aspecific computer system 800, which can represent any server, computeror component, such as consumer 1 (124), consumer N (126), merchant 1(118), merchant M (120), and promotion offering system 102. The computersystem 800 may include an ordered listing of a set of instructions 802that may be executed to cause the computer system 800 to perform any oneor more of the methods or computer-based functions disclosed herein. Thecomputer system 800 can operate as a stand-alone device or can beconnected, e.g., using the network 122, to other computer systems orperipheral devices.

In a networked deployment, the computer system 800 can operate in thecapacity of a server or as a client-user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 800 may alsobe implemented as or incorporated into various devices, such as apersonal computer or a mobile computing device capable of executing aset of instructions 802 that specify actions to be taken by thatmachine, including and not limited to, accessing the Internet or Webthrough any form of browser. Further, each of the systems described caninclude any collection of sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

The computer system 800 can include a memory 803 on a bus 810 forcommunicating information. Code operable to cause the computer system toperform any of the acts or operations described herein can be stored inthe memory 803. The memory 803 may be a random-access memory, read-onlymemory, programmable memory, hard disk drive or any other type ofvolatile or non-volatile memory or storage device.

The computer system 800 can include a processor 801, such as a centralprocessing unit (CPU) and/or a graphics processing unit (GPU). Theprocessor 801 may include one or more general processors, digital signalprocessors, application specific integrated circuits, field programmablegate arrays, digital circuits, optical circuits, analog circuits,combinations thereof, or other now known or later-developed devices foranalyzing and processing data. The processor 801 may implement the setof instructions 802 or other software program, such as manuallyprogrammed or computer-generated code for implementing logicalfunctions. The logical function or any system element described can,among other functions, process and convert an analog data source such asan analog electrical, audio, or video signal, or a combination thereof,to a digital data source for audio-visual purposes or other digitalprocessing purposes such as for compatibility for computer processing.

The computer system 800 can also include a disk or optical drive unit804. The disk drive unit 804 may include a computer-readable medium 805in which one or more sets of instructions 802, e.g., software, may beembedded. Further, the instructions 802 may perform one or more of theoperations as described herein. The instructions 802 may residecompletely, or at least partially, within the memory 803 or within theprocessor 801 during execution by the computer system 800. Accordingly,the databases 110, 112, 114, or 116 may be stored in the memory 803 orthe disk unit 804.

The memory 803 and the processor 801 also may include computer-readablemedia as discussed above. A “computer-readable medium,”“computer-readable storage medium,” “machine readable medium,”“propagated-signal medium,” or “signal-bearing medium” may include anydevice that has, stores, communicates, propagates, or transportssoftware for use by or in connection with an instruction executablesystem, apparatus, or device. The machine-readable medium mayselectively be, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium.

Additionally, the computer system 800 may include an input device 807,such as a keyboard or mouse, configured for a user to interact with anyof the components of system 800. It may further include a display 806,such as a liquid crystal display (LCD), a cathode ray tube (CRT), or anyother display suitable for conveying information. The display 806 mayact as an interface for the user to see the functioning of the processor801, or specifically as an interface with the software stored in thememory 803 or the drive unit 804.

The computer system 800 may include a communication interface 808 thatenables communications via the communications network 122. The network122 may include wired networks, wireless networks, or combinationsthereof. The communication interface 808 network may enablecommunications via any number of communication standards, such as802.11, 802.17, 802.20, WiMax, 802.15.4, cellular telephone standards,or other communication standards, as discussed above. Simply because oneof these standards is listed does not mean any one is preferred.

Further, the promotion offering system 102, as depicted in FIG. 1A maycomprise one computer system or multiple computer systems. Further, theflow diagrams illustrated in the Figures may use computer readableinstructions that are executed by one or more processors in order toimplement the functionality disclosed.

The present disclosure contemplates a computer-readable medium thatincludes instructions or receives and executes instructions responsiveto a propagated signal, so that a device connected to a network cancommunicate voice, video, audio, images or any other data over thenetwork. Further, the instructions can be transmitted or received overthe network via a communication interface. The communication interfacecan be a part of the processor or can be a separate component. Thecommunication interface can be created in software or can be a physicalconnection in hardware. The communication interface can be configured toconnect with a network, external media, the display, or any othercomponents in system, or combinations thereof. The connection with thenetwork can be a physical connection, such as a wired Ethernetconnection or can be established wirelessly as discussed below. In thecase of a service provider server, the service provider server cancommunicate with users through the communication interface.

The computer-readable medium can be a single medium, or thecomputer-readable medium can be a single medium or multiple media, suchas a centralized or distributed database, or associated caches andservers that store one or more sets of instructions. The term“computer-readable medium” can also include any medium that can becapable of storing, encoding or carrying a set of instructions forexecution by a processor or that can cause a computer system to performany one or more of the methods or operations disclosed herein.

The computer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. The computer-readable medium also may be a randomaccess memory or other volatile re-writable memory. Additionally, thecomputer-readable medium may include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an email or other self-containedinformation archive or set of archives may be considered a distributionmedium that may be a tangible storage medium. The computer-readablemedium is preferably a tangible storage medium. Accordingly, thedisclosure may be considered to include any one or more of acomputer-readable medium or a distribution medium and other equivalentsand successor media, in which data or instructions can be stored.

Alternatively or in addition, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments may broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that may be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system may encompass software, firmware, and hardwareimplementations.

The methods described herein may be implemented by software programsexecutable by a computer system. Further, implementations may includedistributed processing, component/object distributed processing, andparallel processing. Alternatively or in addition, virtual computersystem processing may be constructed to implement one or more of themethods or functionality as described herein.

Although components and functions are described that may be implementedin particular embodiments with reference to particular standards andprotocols, the components and functions are not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, andHTTP) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations described herein are intended to provide a generalunderstanding of the structure of various embodiments. The illustrationsare not intended to serve as a complete description of all of theelements and features of apparatus, processors, and systems that utilizethe structures or methods described herein. Many other embodiments canbe apparent to those of skill in the art upon reviewing the disclosure.Other embodiments can be utilized and derived from the disclosure, suchthat structural and logical substitutions and changes can be madewithout departing from the scope of the disclosure. Additionally, theillustrations are merely representational and cannot be drawn to scale.Certain proportions within the illustrations may be exaggerated, whileother proportions may be minimized. Accordingly, the disclosure and thefigures are to be regarded as illustrative rather than restrictive.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the description. Thus, to the maximumextent allowed by law, the scope is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

1-22. (canceled)
 23. A method for generating an electroniccorrespondence comprising a contemplated promotion, the methodcomprising: generating an estimated acceptance by a consumer of thecontemplated promotion; determining a contemplated position from among aplurality of positions within the electronic correspondence at which toposition content for display at a user interface of a consumer device;determining, based in part on a previous presentation data model, acorrection factor based on (1) an elapsed time, (2) a previous position,and (3) the contemplated position, wherein determining the correctionfactor comprises: determining that the contemplated promotion waspreviously presented to the consumer at a previous time; calculating theelapsed time between previously offering the contemplated promotion andoffering the contemplated promotion; determining the previous positionat which the contemplated promotion was previously presented to theconsumer at the previous time; determining, based on the elapsed time,the correction factor; determining a final position from thecontemplated position based on the estimated acceptance and thecorrection factor; generating the electronic correspondence, theelectronic correspondence comprising at least the contemplated promotionlocated in the final position; and causing transmission of theelectronic correspondence, the electronic correspondence configured todisplay real-time webpage content at the user interface of the consumerdevice.
 24. The method of claim 23, wherein determining that thecontemplated promotion was previously presented to the consumercomprises determining that a same promotion was previously presented tothe consumer.
 25. The method of claim 23, wherein determining that thecontemplated promotion was previously presented to the consumercomprises determining that at least one attribute of the contemplatedpromotion matches at least one attribute of the promotion previouslypresented to the consumer.
 26. The method of claim 23, wherein theprevious presentation data model comprises a first look-up tableidentifying a first correction factor value indicative of conversionrates for previous promotions that were presented to a previous consumerafter a previous presentation of the previous promotion to the previousconsumer at configurable elapsed time thresholds.
 27. The method ofclaim 23, wherein the previous presentation data model comprises asecond look-up table identifying a second correction factor valueindicative of conversion rates for previous promotions that werepresented to the consumer at the previous position and at the sameelapsed time.
 28. The method of claim 23, wherein the previouspresentation data model is further configured to input at least oneattribute of the contemplated promotion and output the correction factorindicative of a historical effect of re-offering promotions that sharethe at least one attribute as the contemplated promotion after theamount of time elapsed.
 29. The method of claim 23, wherein the previouspresentation data model is further configured to input at least oneattribute of the consumer and output the correction factor indicative ofa historical effect of re-offering promotions that share the at leastone attribute as the consumer after the amount of time elapsed.
 30. Asystem for generating an electronic correspondence comprising acontemplated promotion, the system comprising: at least one memoryconfigured to store a previous presentation data model and a historicaldata model; and a processor in communication with the at least onememory and configured to: generate an estimated acceptance by a consumerof the contemplated promotion; determine a contemplated position fromamong a plurality of positions within the electronic correspondence atwhich to position content for display at a user interface of a consumerdevice; determine, based in part on a previous presentation data model,a correction factor based on (1) an elapsed time, (2) a previousposition, and (3) the contemplated position, wherein determining thecorrection factor comprises: determining that the contemplated promotionwas previously presented to the consumer at a previous time; calculatingthe elapsed time between previously offering the contemplated promotionand offering the contemplated promotion; determining the previousposition at which the contemplated promotion was previously presented tothe consumer at the previous time; determining, based on the elapsedtime, the correction factor; determine a final position from thecontemplated position based on the correction factor; generate theelectronic correspondence, the electronic correspondence comprising atleast the contemplated promotion located in the final position; andcause transmission of the electronic correspondence, the electroniccorrespondence configured to display real-time webpage content at theuser interface of the consumer device.
 31. The system of claim 30,wherein the determination that the contemplated promotion was previouslypresented to the consumer comprises determining that a same promotionwas previously presented to the consumer.
 32. The system of claim 30,wherein the determination that the contemplated promotion was previouslypresented to the consumer comprises determining that at least oneattribute of the contemplated promotion matches at least one attributeof the promotion previously presented to the consumer.